Legible Transformers turn 78% of units into crisp, editable detectors
The paper builds transformers from bounded, named operators that act like fuzzy set detectors, then forces them to stay crisp during training with a variance-floor loss. The resulting models make 78% of feed‑forward units and half of attention channels interpretable, and edits become 50‑184× more localized, enabling surgical manipulation of concepts.